Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Quantitative diffusion tensor analysis using multiple tensor ellipsoids model and tensor field interpolation at fiber

Hiroyuki Kabasawa1, Yoshitaka Masutani, Osamu Abe

  • 1Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. hkabasaw-tky@umin.ac.jp

Academic Radiology
|December 15, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Two medicolegal autopsy cases of multinodular and vacuolating neuronal tumor revealed by postmortem MRI.

Legal medicine (Tokyo, Japan)·2023
Same author

Pulse Sequences and Reconstruction in Fast MR Imaging of the Liver.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2023
Same author

Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.

Magnetic resonance imaging·2022
Same author

Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes.

European radiology·2022
Same author

Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.

Japanese journal of radiology·2021
Same author

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.

European journal of radiology·2021
Same journal

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

Academic radiology·2026
Same journal

MRI-based Predictors and Risk Constellations of Chronic Ankle Instability After Acute Lateral Ankle Sprain: A Multicenter Study.

Academic radiology·2026
Same journal

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

Academic radiology·2026
Same journal

Evaluating the Impact of Embolization on Outcomes in Iliopsoas Hematomas: A Multicenter Retrospective Propensity-matched Study.

Academic radiology·2026
Same journal

Comparison of Iterative Metal Artifact Reduction Presets In Ultra-high-resolution Photon-counting CT Angiography of Patients with Total Knee Endoprosthesis.

Academic radiology·2026
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
See all related articles

A new diffusion tensor imaging model accurately reconstructs brain white matter fiber crossings using interpolated orientations. This method reduces parameters, improving robustness and efficiency for diffusion parameter calculation in complex brain regions.

Area of Science:

  • Neuroimaging
  • Diffusion Tensor Imaging (DTI)
  • Computational Neuroscience

Background:

  • Standard single-tensor models in DTI are inaccurate in brain regions with intravoxel fiber crossings.
  • Previous models addressing fiber crossings require extensive scan and computational time.
  • Accurate mapping of white matter architecture is crucial for understanding brain function and disease.

Purpose of the Study:

  • To present a novel DTI model utilizing interpolated diffusion tensor orientations.
  • To reduce the number of parameters required for estimating diffusion properties at fiber crossings.
  • To improve the accuracy and efficiency of diffusion parameter calculation in complex white matter regions.

Main Methods:

  • Reconstruction of fiber orientation information using radial basis function-based interpolation.

Related Experiment Videos

  • Comparison of the proposed method with the conventional two-ellipsoid model using synthetic phantom data.
  • Validation of the method's effectiveness on diffusion-weighted imaging data from a healthy volunteer.

    • Main Results:

    • The proposed method yielded significantly higher fractional anisotropy (FA) values in the corpus callosum (0.67) and corticospinal tract (0.65) compared to the standard single-tensor method (0.35).
    • Estimated FA values demonstrated good agreement with adjacent fiber bundles, indicating improved accuracy.
    • Reduced parameter estimation enhanced the robustness of diffusion parameter calculation at fiber crossings.

    Conclusions:

    • The radial basis function-based technique effectively reconstructs diffusion properties in fiber-crossing volumes.
    • The method enables accurate diffusion property estimation from sparse sampling of high angular diffusion weighted images.
    • This approach offers a more efficient and robust solution for analyzing complex white matter architecture in neuroimaging.